AWS Data Engineer
Posted
Summary
Designs, builds, and operates end-to-end data pipelines on Databricks and/or Snowflake for a regulated-sector data platform: workspace administration, governance (Unity Catalog, RBAC, masking), CI/CD with Terraform, monitoring, and migration support. Requires 5+ years data engineering and 3+ years hands-on Databricks.
Title:
AWS Data Engineer
Location:
Singapore
Key Responsibilities
Design, build, and operate end-to-end data pipelines (ingestion → transformation → serving) on Databricks and/or Snowflake, following medallion/layered architecture patterns Configure and administer workspaces/accounts — compute policies, resource sizing, environment setup, and access hierarchy — per the SDP reference architecture Implement data governance controls: catalogue and schema design, RBAC, row/column-level security, data masking, and lineage tracking Set up CI/CD and infrastructure-as-code for pipeline deployment and environment promotion (dev → test → prod) Configure monitoring, telemetry, and audit logging to meet SDP's central observability and security posture requirements Support UAT, integration testing, and parallel-run validation during migration and go-live Produce handover documentation (runbooks, access lists, escalation procedures) for agency operations teams
Required Technical Skills — Databricks
Unity Catalog — catalogue/schema design, access control, and data lineage Lakeflow / Delta Live Tables for pipeline orchestration; Delta Lake table format Databricks SQL and cluster/workspace administration (compute policies, pools, cost management) Databricks Asset Bundles (DABs) and Databricks Repos for CI/CD PySpark / Spark SQL for large-scale data transformation Working knowledge of Databricks system tables (audit logs, billing/usage, query history) for observability Minimum 5 years of hands-on experience in Data Engineering, Data Platform Engineering, or related disciplines. Minimum 3 years of hands-on experience with Databricks involving data pipeline development, platform administration, governance, and optimization.
Required Technical Skills — Common
Strong SQL and Python (PySpark or general-purpose) for data engineering Data modeling — dimensional design, star/snowflake schemas, semantic layers CI/CD pipelines (e.g., Azure DevOps, GitHub Actions, GitLab CI) for data engineering workflows Infrastructure-as-code (Terraform preferred) for provisioning cloud data platform resources Hands-on experience on at least one hyperscaler — AWS, Azure, or Google Cloud Understanding of data security and compliance frameworks
Preferred Qualifications
Databricks Certified Data Engineer Associate/Professional SnowPro Core, or SnowPro Advanced: Data Engineer AWS certification (e.g. AWS Certified Data Engineer - Associate, Solutions Architect) and hands-on experience with AWS data services. Prior experience delivering on a regulated-sector data platform, or exposure to compliance frameworks such as IM8, is an add-on Experience working as part of a System Integrator (SI) delivery team alongside a platform Principal is an add-on
AWS Data Engineer
Location:
Singapore
Key Responsibilities
Design, build, and operate end-to-end data pipelines (ingestion → transformation → serving) on Databricks and/or Snowflake, following medallion/layered architecture patterns Configure and administer workspaces/accounts — compute policies, resource sizing, environment setup, and access hierarchy — per the SDP reference architecture Implement data governance controls: catalogue and schema design, RBAC, row/column-level security, data masking, and lineage tracking Set up CI/CD and infrastructure-as-code for pipeline deployment and environment promotion (dev → test → prod) Configure monitoring, telemetry, and audit logging to meet SDP's central observability and security posture requirements Support UAT, integration testing, and parallel-run validation during migration and go-live Produce handover documentation (runbooks, access lists, escalation procedures) for agency operations teams
Required Technical Skills — Databricks
Unity Catalog — catalogue/schema design, access control, and data lineage Lakeflow / Delta Live Tables for pipeline orchestration; Delta Lake table format Databricks SQL and cluster/workspace administration (compute policies, pools, cost management) Databricks Asset Bundles (DABs) and Databricks Repos for CI/CD PySpark / Spark SQL for large-scale data transformation Working knowledge of Databricks system tables (audit logs, billing/usage, query history) for observability Minimum 5 years of hands-on experience in Data Engineering, Data Platform Engineering, or related disciplines. Minimum 3 years of hands-on experience with Databricks involving data pipeline development, platform administration, governance, and optimization.
Required Technical Skills — Common
Strong SQL and Python (PySpark or general-purpose) for data engineering Data modeling — dimensional design, star/snowflake schemas, semantic layers CI/CD pipelines (e.g., Azure DevOps, GitHub Actions, GitLab CI) for data engineering workflows Infrastructure-as-code (Terraform preferred) for provisioning cloud data platform resources Hands-on experience on at least one hyperscaler — AWS, Azure, or Google Cloud Understanding of data security and compliance frameworks
Preferred Qualifications
Databricks Certified Data Engineer Associate/Professional SnowPro Core, or SnowPro Advanced: Data Engineer AWS certification (e.g. AWS Certified Data Engineer - Associate, Solutions Architect) and hands-on experience with AWS data services. Prior experience delivering on a regulated-sector data platform, or exposure to compliance frameworks such as IM8, is an add-on Experience working as part of a System Integrator (SI) delivery team alongside a platform Principal is an add-on